59 lines
1.9 KiB
Markdown
59 lines
1.9 KiB
Markdown
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# Three Key Components
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1. Representation
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- Declarative & Procedural knowledge
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- Typically human-readable symbols
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2. Reasoning
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- Ability to solve problems
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- Express and solve range of problems and types
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- Make explicit and implicit information known to it
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- Control mechanism to decide which operations to use if and when, when a solution has been found
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3. Learning
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An AI system must be able to
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1. Store knowledge
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2. Apply knowledge to solve problems
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3. Acquire new knowledge through experience
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![[ai-nested-subjects.png]]
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# Expert Systems
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- Usually easier to obtain compiled experience from experts than duplicate experience that made them experts for network
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# Information Processing
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## Inductive
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- General patterns and rules determined from data and experience
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- Similarity-based learning
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## Deductive
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- General rules are used to determine specific facts
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- Proof of a theorem
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Explanation-based learning uses both
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# Classical AI vs Neural Nets
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## Level of Explanation
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- Classical has emphasis on building symbolic representations
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- Models cognition as sequential processing of symbolic representations
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- Neural nets emphasis on parallel distributed processing models
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- Models assume information processing takes place through interactions of large numbers of neurons
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## Processing style
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- Classical processing is sequential
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- Von Neumann Machine
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- Neural nets use parallelism everywhere
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- Source of flexibility
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- Robust
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## Representational Structure
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- Classical emphasises language of thought
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- Symbolic representation has quasi-linguistic structure
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- New symbols created from compositionality
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- Neural nets have problem describing nature and structure of representation
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Symbolic AI is the formal manipulation of a language of algorithms and data representations in a top-down fashion
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Neural nets bottom-up
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![[ai-io.png]]
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